// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli    Codeplay Software Ltd.
// Ralph Potter  Codeplay Software Ltd.
// Luke Iwanski  Codeplay Software Ltd.
// Contact: <eigen@codeplay.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL

#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>

using Eigen::Tensor;
typedef Tensor<float, 1>::DimensionPair DimPair;

template<typename DataType, int DataLayout, typename IndexType>
void
test_sycl_cumsum(const Eigen::SyclDevice& sycl_device,
				 IndexType m_size,
				 IndexType k_size,
				 IndexType n_size,
				 int consume_dim,
				 bool exclusive)
{
	static const DataType error_threshold = 1e-4f;
	std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << " consume_dim : " << consume_dim << ")"
			  << std::endl;
	Tensor<DataType, 3, DataLayout, IndexType> t_input(m_size, k_size, n_size);
	Tensor<DataType, 3, DataLayout, IndexType> t_result(m_size, k_size, n_size);
	Tensor<DataType, 3, DataLayout, IndexType> t_result_gpu(m_size, k_size, n_size);

	t_input.setRandom();
	std::size_t t_input_bytes = t_input.size() * sizeof(DataType);
	std::size_t t_result_bytes = t_result.size() * sizeof(DataType);

	DataType* gpu_data_in = static_cast<DataType*>(sycl_device.allocate(t_input_bytes));
	DataType* gpu_data_out = static_cast<DataType*>(sycl_device.allocate(t_result_bytes));

	array<IndexType, 3> tensorRange = { { m_size, k_size, n_size } };
	TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_t_input(gpu_data_in, tensorRange);
	TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_t_result(gpu_data_out, tensorRange);
	sycl_device.memcpyHostToDevice(gpu_data_in, t_input.data(), t_input_bytes);
	sycl_device.memcpyHostToDevice(gpu_data_out, t_input.data(), t_input_bytes);

	gpu_t_result.device(sycl_device) = gpu_t_input.cumsum(consume_dim, exclusive);

	t_result = t_input.cumsum(consume_dim, exclusive);

	sycl_device.memcpyDeviceToHost(t_result_gpu.data(), gpu_data_out, t_result_bytes);
	sycl_device.synchronize();

	for (IndexType i = 0; i < t_result.size(); i++) {
		if (static_cast<DataType>(std::fabs(static_cast<DataType>(t_result(i) - t_result_gpu(i)))) < error_threshold) {
			continue;
		}
		if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), error_threshold)) {
			continue;
		}
		std::cout << "mismatch detected at index " << i << " CPU : " << t_result(i) << " vs SYCL : " << t_result_gpu(i)
				  << std::endl;
		assert(false);
	}
	sycl_device.deallocate(gpu_data_in);
	sycl_device.deallocate(gpu_data_out);
}

template<typename DataType, typename Dev>
void
sycl_scan_test_exclusive_dim0_per_device(const Dev& sycl_device)
{
	test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 2049, 1023, 127, 0, true);
	test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 2049, 1023, 127, 0, true);
}
template<typename DataType, typename Dev>
void
sycl_scan_test_exclusive_dim1_per_device(const Dev& sycl_device)
{
	test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 2049, 127, 1, true);
	test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 2049, 127, 1, true);
}
template<typename DataType, typename Dev>
void
sycl_scan_test_exclusive_dim2_per_device(const Dev& sycl_device)
{
	test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 127, 2049, 2, true);
	test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 127, 2049, 2, true);
}
template<typename DataType, typename Dev>
void
sycl_scan_test_inclusive_dim0_per_device(const Dev& sycl_device)
{
	test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 2049, 1023, 127, 0, false);
	test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 2049, 1023, 127, 0, false);
}
template<typename DataType, typename Dev>
void
sycl_scan_test_inclusive_dim1_per_device(const Dev& sycl_device)
{
	test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 2049, 127, 1, false);
	test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 2049, 127, 1, false);
}
template<typename DataType, typename Dev>
void
sycl_scan_test_inclusive_dim2_per_device(const Dev& sycl_device)
{
	test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 127, 2049, 2, false);
	test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 127, 2049, 2, false);
}
EIGEN_DECLARE_TEST(cxx11_tensor_scan_sycl)
{
	for (const auto& device : Eigen::get_sycl_supported_devices()) {
		std::cout << "Running on " << device.template get_info<cl::sycl::info::device::name>() << std::endl;
		QueueInterface queueInterface(device);
		auto sycl_device = Eigen::SyclDevice(&queueInterface);
		CALL_SUBTEST_1(sycl_scan_test_exclusive_dim0_per_device<float>(sycl_device));
		CALL_SUBTEST_2(sycl_scan_test_exclusive_dim1_per_device<float>(sycl_device));
		CALL_SUBTEST_3(sycl_scan_test_exclusive_dim2_per_device<float>(sycl_device));
		CALL_SUBTEST_4(sycl_scan_test_inclusive_dim0_per_device<float>(sycl_device));
		CALL_SUBTEST_5(sycl_scan_test_inclusive_dim1_per_device<float>(sycl_device));
		CALL_SUBTEST_6(sycl_scan_test_inclusive_dim2_per_device<float>(sycl_device));
	}
}
